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Add doc embedding implementation during inference #339

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Add doc embedding implementation during inference #339

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10 changes: 9 additions & 1 deletion neuralcoref/neuralcoref.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -714,7 +714,7 @@ cdef class NeuralCoref(object):
# if debug: print("Build single features and pair features arrays")
# ''' Build single features and pair features arrays '''
doc_c = doc.c
doc_embedding = numpy.zeros(SIZE_EMBEDDING, dtype='float32') # self.embeds.get_average_embedding(doc.c, 0, doc.length + 1, self.hashes.puncts)
doc_embedding = self.get_doc_embedding(doc)
doc_embed = doc_embedding
for i in range(n_mentions):
s_inp_arr[i, :SGNL_FEATS_0] = self.get_mention_embeddings(mentions[i], doc_embedding) # Set embeddings
Expand Down Expand Up @@ -872,6 +872,14 @@ cdef class NeuralCoref(object):
def normalize(self, Token token):
return self.hashes.digit_word if token.is_digit else token.lower

def get_doc_embedding(self, Doc doc):
embed_arr = numpy.zeros(self.static_vectors.shape[1], dtype='float32')
for sent in doc.sents:
utt_embed = self.get_average_embedding(sent)
embed_arr += utt_embed
embed_arr = numpy.divide(embed_arr, float(max(len(list(doc.sents)), 1)))
return embed_arr

def get_static(self, hash_t word):
return self.static_vectors[word] if word in self.static_vectors else self.static_vectors[self.hashes.unknown_word]

Expand Down